The accurate estimation of entropy-based measures is generally difficult. There is no consensus on an optimal way for estimating TE from a dataset [1]}. Schreiber proposed the TE using correlation integrals [2]}. The most common TE estimation approach is histogram estimation with fixed partitioning. This simple method is not scalable for more than three scalars. Moreover, it is sensitive to the size of bins used. Other nonparametric entropy estimation methods have been also used for computing the TE [3]}, [1]}, [5]}: kernel density estimation methods, nearest-neighbor, Parzen, neural networks, etc.
